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Simulated annealing based artificial bee colony algorithm for global numerical optimization
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DOI：10.1016/j.amc.2012.09.052.png)
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Abstract
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Artificial bee colony (ABC) algorithm is a global optimization algorithm, which has been shown to be competitive with some conventional swarm algorithm, such as genetic algorithm (GA) and particle swarm optimization (PSO). However, there is still an insufficiency in ABC algorithm, in that it has poor convergence rate in some situations. Inspired by simulated annealing algorithm, a simulated annealing based ABC algorithm (SAABC) is proposed. Simulated annealing algorithm is introduced into employed bees search process to improve the exploitation of the algorithm. The experimental results are tested on a set of numerical benchmark functions with different dimensions. That show that SAABC algorithm can outperform ABC and global best guided ABC algorithms in most of the experiments. (C) 2012 Elsevier Inc. All rights reserved.
Keywords:
Artificial bee colony
Simulated annealing algorithm
Global best guided ABC
Global numerical optimization
Swarm intelligence
Optimization
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